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《Geospatial Information》 2018-06
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LiDAR Point Cloud Multi-feature Classification Method Based on the Fusion Image Information

YE Gang;  
Because that the classification accuracy of single Li DAR point cloud data was not high due to the complexity and category diversity of the object, we put forward a Li DAR point cloud multi-feature classification method based on the fusion image information. We accomplished this method as the three steps. Firstly, we determined the feature space according to the requirements of application purpose and feature classification by analyzing the spectral and shape features of the image, and the geometrical features of Li DAR data, which were as a prior knowledge of corresponding classification rule set. And then, according to the distance between feature descriptors, we determined the space spatial clustering. Finally, we obtained the point cloud classification of five categories(e.g. buildings, trees, grass, road and uncertain features). The classification accuracy is 95.3%, the kappa coefficient is 0.935. In addition, we introduced the SVM method based on image classification, and point cloud classification method based on Terrasolid software respectively to verify the effectiveness of the algorithm in this paper.
【Fund】: 国家科技支撑计划(2014BAL05B00)
【CateGory Index】: P237
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